Deep Predictive Learning: Motion Learning Concept inspired by Cognitive Robotics

Understand

Bridging the gap between motion models and reality is crucial by using limited data to deploy robots in the real world.

• Deep learning is expected to be generalized to diverse situations while reducing feature design costs through end-to-end learning for environmental recognition and motion generation.
• However, data collection for model training is costly, and time and human resources are essential for robot trial-and-error with physical contact.
• We propose "Deep Predictive Learning," a motion learning concept that predicts the robot's sensorimotor dynamics, assuming imperfections in the prediction model.